Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm

Abstract

English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students’ English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.

EDAS Method for Single‐Valued Neutrosophic Number Multiattribute Group Decision‐Making and Applications to Physical Education Teaching Quality Evaluation in Colleges and Universities

“…For this mentioned reason, the second study incentive is to produce the novel weight decisions, which could cope with completely unknown decision weight. (3) Teaching quality is the eternal theme of college construction and development, especially in the current transitional period when higher education is gradually transitioning from elite education to mass education [58][59][60][61][62]. Terefore, it is urgent to design exploratory research to enrich the research methods of PE teaching quality evaluation.…”

Application of BP Neural Network in Teaching Quality Evaluation of Higher Vocational Education

“…Liu and Yin proposed a hybrid intelligence algorithm based on a genetic algorithm and reverse propagation neural network to assess the quality level of teaching, and play an intelligent algorithm in the evaluation model construction [17]. Considering the advantages of BP neural networks in evaluation [18], this study uses BP neural networks to analyze HVE evaluation indexes to establish a better evaluation index system and provide more accurate results.…”